1. Field of the Invention
This invention relates to a device and a process for operating a rechargeable storage for electrical energy.
2. Description of Related Art
A survey of currently used charging strategies and charging devices for rechargeable electrochemical batteries can be found in Halaczek/Radecke Batteries and Charging Concepts, 2nd edition, 1998, Franzis"" Verlag GmbH, pages 154 to 236. Conventionally, the approach is to choose a certain charging strategy depending on the battery type, wherein, during charging, the voltage and/or the current of the charging device is controlled depending on the time which has elapsed since the start of the charging process, the measured instantaneous battery voltage, the measured instantaneous current flowing in the charging circuit, and/or the measured instantaneous battery temperature, i.e., for control of the charging process, the instantaneous measured values relating to the battery or quantities derived therefrom (for example, the variation as a function of time) are used. These known processes and devices are disadvantageous in that, generally, the instantaneous charging state and the instantaneous qualitative state of the battery cannot be determined from the instantaneous measured values for voltage, current and temperature so accurately that a charging strategy which is optimized with respect, for example, to charging time or service life of the battery can be found.
On pages 246 to 248 of the aforementioned reference, digital charging devices are described in which the charging state of the battery is determined by measuring the discharge current and the temperature of the battery at regular intervals during discharging, the results being stored in an internal interrogated E2PROM.
U.S. Pat. No. 5,256,957 discloses a charging process in which a characteristic parameter which is derived from the equivalent network of the battery and which is determined continuously from the currently measured charging current and the currently measured battery voltage is used to determine the shut-off time for the charging process. Here, the charging process is terminated as soon as the variation over time of the characteristic parameter approaches zero. In this case too, the charging strategy is determined from the instantaneous measured values of the battery parameters.
xe2x80x9cDetermination Of State-Of-Charge And State-Of-Health Of Batteries By Fuzzy Logic Methodologyxe2x80x9d by Salkind et al., Journal of Power Sources 80, 1999, pages 293 to 300, discloses using fuzzy logic to predict the charging state of an electrochemical battery based on frequency-dependent resistance measurements (electrochemical impedance spectroscopy) of the battery. The membership function and rule set of the fuzzy logic are found using a neural network.
In xe2x80x9cPredicting Failure Of Secondary Batteriesxe2x80x9d by M. Urquidi-Macdonald et al., Journal of Power Sources 74, 1998, pages 87 to 98, it is proposed that the future discharge behavior of an electrochemical battery, i.e., the voltage behavior over time for a stipulated charging current behavior at a certain temperature, be predicted by means of a neural network, the neural network being trained with already conducted measurements of the charging and discharging cycles (current, voltage, temperature). Here, it is recommended that the amount of data used for training be reduced, for example, by a wavelet transform.
The primary object of this invention is to devise a device and a process for operating a rechargeable storage for electrical energy, using an optimized charging strategy, which, for example, enables a maximized efficient storage service life.
This and other objects are achieved by a device and process for operating a rechargeable storage for electrical energy and a process in accordance with the invention by implementation of an adaptive model of the storage which is automatically optimized continuously using the data acquired in operation. In this way, it always is possible to describe, know and predict the storage state as precisely as possible, and as a result of accurate and continually updated knowledge of the storage state, the charging strategy which is most favorable at the instant can be chosen.
Preferably the adaptive model is implemented by means of neural networks and/or fuzzy logic.
In the following, advantageous embodiments of the invention are explained in detail with reference to the accompanying drawings.